Gaze Fusion-Deep Neural Network Model for Glaucoma Detection

Krishnan, Sajitha; Amudha, J.; Tejwani, Sushma

doi:10.1007/978-981-16-0419-5_4

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Eye Tracking helps to measure cognitive load, concentration, focus, drowsiness, consciousness, and other mental states [50], [59]. Technologies to track the eye have become efficient, cheap, and compact and are increasing use in many fields, including gaming, driver safety, military, education [60], product recommendation system, psychology research, cognitive studies [51], market analysis, medical research [52], [61] [62], advertising [63], and healthcare [64], [65]. [66], [67].…”

Section: A Data and Feature Engineeringmentioning

confidence: 99%

“…Eye tracking can measure cognitive load, concentration, focus, drowsiness, consciousness, and other mental states important in identifying and managing mental health issues. Eye tracking is becoming increasingly important in mental health research, providing valuable information about emotional responses and mechanisms underlying human behavior [50], [52], [58], [59], [61], [64], [65].…”

Section: ) Summarymentioning

confidence: 99%

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PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

Jyotsna,

Amudha,

Ram

et al. 2023

IEEE Access

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Mental health is vital for emotional, psychological, and social well-being. Mental illness can affect thoughts, feelings, and behaviors. Early intervention and specialized care can manage major mental illnesses. Predicting mental state accurately can facilitate behavioral changes and promote overall wellbeing. The paper proposes a novel personalized time series model called PredictEYE, which aims to predict a person's mental state and identify the specific scene responsible for that mental state. The model achieves this by analyzing individuals' eye-tracking time series data while watching calm and stressful videos. The model utilizes deep learning time-series univariate regression model based on Long Short-Term Memory for predicting the future sequence of each feature and a machine learning-based Random Forest algorithm for the mental state prediction. The model's performance was compared across the state-of-the-art literature survey.The predictEYE model could achieve an accuracy of 86.4% accuracy in predicting mental state. Tailoring eye tracking models to individual differences is more effective in comprehending mental states than models that make comparisons across multiple participants, given eye tracking data's unique and distinctive idiosyncratic nature. The eye tracking features play a crucial role in predicting the mental state, and the model is adaptable to work with webcam-based eye tracking and can relate to applications where continuous and non-invasive monitoring is required.

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Section: A Data and Feature Engineeringmentioning

confidence: 99%

Section: ) Summarymentioning

confidence: 99%

PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

Jyotsna,

Amudha,

Ram

et al. 2023

IEEE Access

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“…Gaze Fusion Map (GFM) map was generated by fusing relevant information of 30 images [36]. It is the outcome of monocular performance of different participants by fusing 'hit/miss' of 30 images.…”

Section: ) Visualizationmentioning

confidence: 99%

Gaze Exploration Index (GE i)-Explainable Detection Model for Glaucoma

2022

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Glaucoma is a type of visual impairment that is caused due to damage in the optic nerve. The vision loss increases from the peripheral vision towards the central vision, leading to blindness if untreated. The proposed approach is a Computer-Aided Detection (CADe) system using deep learning to screen visual field loss in glaucoma patients while performing different day-to-day activities such as searching objects, viewing photographs, etc. Incorporating an eye-tracking device helps to identify eye movements of glaucoma patients while performing different activities. Different day-to-day activities are depicted in the form of visual exploration tasks. CADe system fuses performance parameters and eye gaze parameters during visual exploration tasks onto images, to guide health care professionals of primary eye care centers in glaucoma screening. The pertinent eye gaze and performance parameters are visualized in the form of three fusion maps: Gaze Fusion Map (GFM), Gaze Fusion Reaction Time (GFRT) map, Gaze Convex Hull Map (GCHM), which are the outcomes of different visual exploration tasks. In addition, the explainability techniques applied in CADe generated Gaze Exploration -index (GE-i) that discriminates glaucoma and normal.

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